code: def pmin_sampled(mean, var, n_samples=1000, rng=None):
      """Probability that each Gaussian-dist R.V. is less than the others
  
      :param vscores: mean vector
      :param var: variance vector
  
      This function works by sampling n_samples from every (gaussian) mean distribution,
      and counting up the number of times each element's sample is the best.
  
      """
      if rng is None:
          rng = numpy.random.RandomState(232342)
  
      samples = rng.randn(n_samples, len(mean)) * numpy.sqrt(var) + mean
      winners = (samples.T == samples.min(axis=1)).T
      wincounts = winners.sum(axis=0)
      assert wincounts.shape == mean.shape
      return old_div(wincounts.astype("float64"), wincounts.sum())
  
imports: from __future__ import absolute_import
  from __future__ import division
  from future import standard_library
  from builtins import str
  from builtins import range
  from past.builtins import basestring
  from past.utils import old_div
  import datetime
  import numpy
  import logging
  import os
  import shutil
  import numpy
  from .None import pyll
  from contextlib import contextmanager
label_desc: Use image node_id 6 for calling a global function with example usage: pmin_sampled(mean, var, n_samples, rng) and returns: old_div